A statistical perspective on higher-order interactions modeling

TL;DR

This paper reviews the importance of modeling higher-order interactions using hypergraphs, emphasizing their statistical representation and challenges in complex systems analysis.

Contribution

It provides a comprehensive overview of hypergraph modeling from a statistical perspective, including foundational concepts, models, and open research challenges.

Findings

HOI are prevalent in real-world systems involving groups of entities.

Hypergraphs effectively represent complex higher-order relationships.

The paper discusses statistical models and clustering methods for hypergraphs.

Abstract

Modeling higher-order interactions (HOI) has emerged as a crucial challenge in complex systems analysis, as many phenomena cannot be fully captured by pairwise relationships alone. Hypergraphs, which generalize graphs by allowing interactions among more than two entities, provide a powerful framework for representing such intricate dependencies. Adopting a statistical and probabilistic perspective on hypergraph modeling, we propose a guided tour through this emerging research area. We begin by illustrating the ubiquity of HOI in real-world systems, where interactions often involve groups of entities rather than isolated pairs. We then introduce the foundational concepts and notations of hypergraphs, discussing their descriptive statistics, graph-based representations, and the challenges associated with their complexity. We further explore a variety of statistical models for hypergraphs and address the critical task of node clustering. We conclude by outlining some open challenges in the field.